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相关概念视频

Upsampling01:22

Upsampling

262
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
262
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

110
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
110
Sampling Theorem01:15

Sampling Theorem

382
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
382
Aliasing01:18

Aliasing

161
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
161
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

101
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
101
Bandpass Sampling01:17

Bandpass Sampling

206
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
206

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Continuous-Wave Propagation Channel-Sounding Measurement System - Testing, Verification, and Measurements
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一个改进的SAMP算法用于OFDM系统中的稀疏通道估计.

Hao Hu1, Xu Zhao2, Shiyong Chen1

  • 1School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China.

Sensors (Basel, Switzerland)
|August 12, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种改进的SAMP (ImpSAMP) 算法,用于在正交频率分割复杂化 (OFDM) 系统中更好地进行稀疏通道估计. 新方法提高了道状态信息估计的效率和准确性.

关键词:
频道估计 频道估计压缩感应传感器 压缩感应德诺瓦斯 (Denoise) 是一个步骤大小调整调整的步骤大小.

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科学领域:

  • 信号处理 信号处理
  • 无线通信无线通信
  • 信息理论 信息理论

背景情况:

  • 直角频率分割多重复合 (OFDM) 系统需要高效的通道估计,以优化频谱利用.
  • 传统的算法,如Sparse Adaptive Matching Pursuit (SAMP),由于固定的步骤大小,在估计效率和准确性方面面临限制.
  • 在OFDM系统中,可以通过压缩传感技术来降低飞行员的空头成本.

研究的目的:

  • 提出一个改进的SAMP (ImpSAMP) 算法,用于在OFDM系统中增强稀疏通道估计.
  • 解决传统的SAMP算法在估计效率和准确性方面的局限性.
  • 通过减少飞行员开销,提高频谱资源利用率.

主要方法:

  • 实施了使用能量检测的无声化步骤,以减轻频道估计期间的干扰.
  • 引入了基于邻近稀疏通道系数差异的l2规范的动态步骤大小调整机制.
  • 使用双门判断策略进一步提高估计效率.

主要成果:

  • 与传统的SAMP算法相比,拟议的ImpSAMP算法表现出优越的性能.
  • 使用ImpSAMP算法观察到估计效率和准确性的显著改善.
  • 无声化和动态步骤大小调整有效地减少了干扰并加速了趋同.

结论:

  • ImpSAMP算法为OFDM系统中的道估计提供了一种更有效,更准确的方法.
  • 动态步骤大小调整和无效化是提高ImpSAMP性能的关键因素.
  • 这种改进的方法有助于在无线通信系统中更好地利用频谱资源.